Mathematical modelling has become an indispensable tool for engineers, scientists, planners, decision makers and many other professionals to make predictions of future scenarios as well as real impending events. As the modelling approach and the model to be used are problem specific, no single model or approach can be used to solve all problems, and there are constraints in each situation. Modellers therefore need to have a choice when confronted with constraints such as lack of sufficient data, resources, expertise and time.

Environmental and Hydrological Systems Modelling provides the tools needed by presenting different approaches to modelling the water environment over a range of spatial and temporal scales. Their applications are shown with a series of case studies, taken mainly from the Asia-Pacific Region. Coverage includes:

  • Population dynamics
  • Reaction kinetics
  • Water quality systems
  • Longitudinal dispersion
  • Time series analysis and forecasting
  • Artificial neural networks
  • Fractals and chaos
  • Dynamical systems
  • Support vector machines
  • Fuzzy logic systems
  • Genetic algorithms and genetic programming

This book will be of great value to advanced students, professionals, academics and researchers working in the water environment.

chapter Chapter 1|8 pages


chapter Chapter 2|52 pages

Historical development of hydrological modelling

chapter Chapter 3|16 pages

Population dynamics

chapter Chapter 4|8 pages

Reaction kinetics

chapter Chapter 5|32 pages

Water quality systems

chapter Chapter 6|22 pages

Longitudinal dispersion

chapter Chapter 7|72 pages

Time series analysis and forecasting

chapter Chapter 8|76 pages

Artificial neural networks

chapter Chapter 9|34 pages

Radial basis function (RBF) neural networks

chapter Chapter 10|44 pages

Fractals and chaos

chapter Chapter 11|48 pages

Dynamical systems approach of modelling

chapter Chapter 12|24 pages

Support vector machines

chapter Chapter 13|52 pages

Fuzzy logic systems

chapter Chapter 14|8 pages

Genetic algorithms (GAs) and genetic programming (GP)